code: def load_test_model(opt, device_id=0):
      """Read in multiple models for ensemble."""
      shared_vocabs = None
      shared_model_opt = None
      models = []
      for model_path in opt.models:
          vocabs, model, model_opt = onmt.model_builder.load_test_model(
              opt, device_id, model_path=model_path
          )
          if shared_vocabs is None:
              shared_vocabs = vocabs
          else:
              assert (
                  shared_vocabs["src"].tokens_to_ids
                  == vocabs["src"].tokens_to_ids
              ), "Ensemble models must use the same vocabs "
          models.append(model)
          if shared_model_opt is None:
              shared_model_opt = model_opt
      ensemble_model = EnsembleModel(models, opt.avg_raw_probs)
      return shared_vocabs, ensemble_model, shared_model_opt
  
imports: import torch
  import torch.nn
  from onmt.encoders.encoder import EncoderBase
  from onmt.decoders.decoder import DecoderBase
  from onmt.models import NMTModel
  import onmt.model_builder
label_desc: Use image node_id 1 for calling a global function with example usage: load_test_model(opt, device_id) and returns: shared_vocabs,  ensemble_model,  shared_model_opt